<p>Tesla is seeking a Machine Learning Engineer to join our cross-functional team of ML and software engineers focused on building and scaling intelligent scheduling systems that drive service operations across our global network. This includes solving complex, high-impact problems such as vehicle routing, resource allocation, demand forecasting, and operational optimization.</p> <p>In this role, your primary focus will be using and integrating state-of-the-art AI tools - including large language models (LLMs) and autonomous agents - to build intelligent systems that enhance and automate scheduling workflows. You'll be responsible for bringing AI capabilities into production through robust engineering, experimentation, and continuous improvement.</p> <p>You'll collaborate with a team that blends software engineering, data science, and applied ML to deliver scalable solutions that operate in real-world environments.</p> <ul> <li>Design, build, and maintain production-grade intelligent scheduling systems powered by AI tools like large language models and agentic frameworks </li><li>Own the full lifecycle of ML and optimization models - from exploration and prototyping to deployment, monitoring, and continuous improvement </li><li>Develop workflows that leverage AI to improve decision-making, automate planning, and adapt to real-time operational constraints </li><li>Implement prompt engineering and orchestration techniques to improve performance, reliability, and user alignment </li><li>Conduct exploratory data analysis (EDA) to uncover insights, guide modeling decisions, and inform product direction </li><li>Contribute to system architecture and design, especially where AI, automation, and operational scale intersect </li><li>Collaborate cross-functionally to ensure solutions align with system design, operational needs, and end-user impact </li><li>Apply software engineering best practices - including version control, testing, CI/CD, and containerization - to ML and data pipelines </li><li>Degree in Computer Science, Artificial Intelligence, Data Science, Operations Research, Applied Math, or a related field (MS/PhD a plus), or related field, or equivalent experience </li><li>5+ years of work experience in training and maintaining production ML models and data pipelines for real world applications </li><li>Expertise with large language models, AI agents, prompt engineering, and AI platforms (e.g. OpenAI, HuggingFace, LangChain, LlamaIndex, etc.) </li><li>Strong foundation in statistics, probability, ML, and optimization </li><li>Proficiency in Python data tools (e.g., SQL, pandas, numpy, Jupyter, matplotlib) </li><li>Familiarity with Golang or a willingness to learn it quickly </li><li>Proven history of delivering data-driven projects from inception through deployment </li><li>Experience with logistics, supply chain, or operations tech is a plus </li><li>Exceptional communication and collaboration skills across disciplines </li><li>A strategic and proactive mindset - comfortable with ambiguity and ownership of end-to-end project delivery </li></ul>